Browsing by Author "Lin, Wen-Chieh"

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A Gesture System for Graph Visualization in Virtual Reality Environments
(2017-01-01)
Huang, Yi-Jheng
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Fujiwara, Takanori
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Lin, Yun-Xuan
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Lin, Wen-Chieh
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Ma, Kwan-Liu
As virtual reality (VR) hardware technology becomes more mature and affordable, it is timely to develop visualization applications making use of such technology. How to interact with data in an immersive 3D space is both an interesting and challenging problem, demanding more research investigations. In this paper, we present a gesture input system for graph visualization in a stereoscopic 3D space. We compare desktop mouse input with gesture input with bare hands for performing a set of tasks on graphs. Our study results indicate that users are able to effortlessly manipulate and analyze graphs using gesture input. Furthermore, the results also show that using gestures is more efficient when exploring the complicated graph.
A lattice-based MRF model for dynamic near-regular texture tracking
(2007-05-01)
Lin, Wen-Chieh
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Liu, Yanxi
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資訊工程學系
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Department of Computer Science
A near-regular texture ( NRT) is a geometric and photometric deformation from its regular origin-a congruent wallpaper pattern formed by 2D translations of a single tile. A dynamic NRT is an NRT under motion. Although NRTs are pervasive in man-made and natural environments, effective computational algorithms for NRTs are few. This paper addresses specific computational challenges in modeling and tracking dynamic NRTs, including ambiguous correspondences, occlusions, and drastic illumination and appearance variations. We propose a lattice-based Markov-Random-Field ( MRF) model for dynamic NRTs in a 3D spatiotemporal space. Our model consists of a global lattice structure that characterizes the topological constraint among multiple textons and an image observation model that handles local geometry and appearance variations. Based on the proposed MRF model, we develop a tracking algorithm that utilizes belief propagation and particle filtering to effectively handle the special challenges of the dynamic NRT tracking without any assumption on the motion types or lighting conditions. We provide quantitative evaluations of the proposed method against existing tracking algorithms and demonstrate its applications in video editing.
A METHOD TO ENHANCE THE DEEP LEARNING IN AN AERIAL IMAGE
(2017-01-01)
Chou, Kuang-Pen
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Li, Dong-Lin
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Lin, Wen-Chieh
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Prasad, Mukesh
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Lin, Chin-Teng
In this paper, we propose a kind of pre-processing method which can be applied to the depth learning method for the characteristics of aerial image. This method combines the color and spatial information to do the quick background filtering. In addition to increase execution speed, but also to reduce the rate of false positives
A Motor Imagery Based Brain-Computer Interface System via Swarm-Optimized Fuzzy Integral and Its Application
(2016)
Wu, Shang-Lin
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Liu, Yu-Ting
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Chou, Kuang-Pen
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Lin, Yang-Yin
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Lu, Jie
A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications.
A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications
(1970-01-01)
Chou, Kuang Pen
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Prasad, Mukesh
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Yang, Jie
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Su, Sheng-Yao
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Tao, Xian
Face detection often plays the first step in various visual applications. Large variants of facial deformations due to head movements and facial expression make it difficult to identify appropriate face region. In this paper, a robust real-time face alignment system, including facial landmarks detection and face rectification, is proposed. A facial landmarks detection model based on regression tree is utilized in the proposed system. In face rectification framework, 2-D geometrical analysis based on pitch, yaw and roll movements is designed to solve the misalignment problem in face detection. The experiments on the two datasets verify the performance significantly improved by the proposed method in the facial recognition task and outperform than those obtained by other alignment methods. Furthermore, the proposed method can achieve robust recognition results even if the amount of training images is not large.
A self-adaptive artificial bee colony algorithm with local search for TSK-type neuro-fuzzy system training
(2019-01-01)
Chou, Kuang-Pen
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Lin, Chin-Teng
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Lin, Wen-Chieh
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資訊工程學系
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Department of Computer Science
In this paper, we introduce a self-adaptive artificial bee colony (ABC) algorithm for learning the parameters of a Takagi-Sugeno-Kang-type (TSK-type) neuro-fuzzy system (NFS). The proposed NFS learns fuzzy rules for the premise part of the fuzzy system using an adaptive clustering method according to the input-output data at hand for establishing the network structure. All the free parameters in the NFS, including the premise and the following TSK-type consequent parameters, are optimized by the modified ABC (MABC) algorithm. Experiments involve two parts, including numerical optimization problems and dynamic system identification problems. In the first part of investigations, the proposed MABC compares to the standard ABC on mathematical optimization problems. In the remaining experiments, the performance of the proposed method is verified with other metaheuristic methods, including differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and standard ABC, to evaluate the effectiveness and feasibility of the system. The simulation results show that the proposed method provides better approximation results than those obtained by competitors methods.
A Visual Attention Monitor Based on Steady-State Visual Evoked Potential
(2016-03)
Lee, Yi-Chieh
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Lin, Wen-Chieh
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Cherng, Fu-Yin
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Ko, Li-Wei
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生物科技學系
Attention detection is important for many applications. Automatic determination of users\' visual attention state is challenging because attention involves numerous complex and internal human cognitive functions. Behavioral observations, such as eye gaze or response to external stimuli, can provide clues for users\' visual attention state; however, users\' cognitive state cannot be easily known. Conventional electroencephalography-based methods detect attention by observing the dynamic changes in the frontal lobe of the brain, especially in the anterior cingulate cortex (ACC). However, that area in the brain is associated with many functions, some of which correlate with conscious experience but are not directly related to attention. In this paper, we design an attention monitoring system to detect whether the brain experiences a visual stimulus consciously. Our experiments verified the feasibility of our design, and the average classification rate ranged from 72% to 82%.
An Artist Friendly Material Design System
(2017-03-01)
Huang, Tsung-Shian
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Liao, Jen-Hao
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Lin, Wen-Chieh
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Chuang, Jung-Hong
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資訊工程學系
Material design is an important task in game and animation industry. It often requires artist's great efforts to create an elaborate material. Although several successful material editing systems already exist, it is still hard for artists to design a material on their own since these systems usually have neither an intuitive and sufficiently sophisticated material representation nor an artist-friendly editing interface. In this paper, we propose a simple material creation flow and develop a material design system based on an intuitive material representation. With the system, an artist can bring their creativity into play on designing materials that are realistic or imaginary.
An EEG-based Approach for Evaluating Graphic Icons from the Perspective of Semantic Distance
(2015)
Cherng, Fu-Yin
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Lin, Wen-Chieh
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King, Jung-Tai
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Lee, Yi-Chen
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資訊工程學系
Graphic icons play an increasingly important role in interface design due to the proliferation of digital devices in recent years. Their ability to express information in a universal fashion allows us to immediately interact with new applications, systems, and devices. Icons can, however, cause user confusion and frustration if designed poorly. Several studies have evaluated icons using behavioral-performance metrics such as reaction time as well as self-report methods. However, determining the usability of icons based on behavioral measures alone is not straightforward, because users\' interpretations of the meaning of icons involve various cognitive processes and perceptual mechanisms. Moreover, these perceptual mechanisms are affected not only by the icons themselves, but by usage scenarios. Thus, we need a means of sensitively and continuously measuring users\' different cognitive processes when they are interacting with icons. In this study, we propose an EEG-based approach to icon evaluation, in which users\' EEG signals are measured in multiple usage scenarios. Based on a combination of EEG and behavioral results, we provide a novel interpretation of the participants\' perception during these tasks, and identify some important implications for icon design.
An EEG-based Approach for Evaluating Graphic Icons from the Perspective of Semantic Distance
(2016)
Cherng, Fu-Yin
;
Lin, Wen-Chieh
;
King, Jung-Tai
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Lee, Yi-Chen
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資訊工程學系
Graphic icons play an increasingly important role in interface design due to the proliferation of digital devices in recent years. Their ability to express information in a universal fashion allows us to immediately interact with new applications, systems, and devices. Icons can, however, cause user confusion and frustration if designed poorly. Several studies have evaluated icons using behavioral-performance metrics such as reaction time as well as self-report methods. However, determining the usability of icons based on behavioral measures alone is not straightforward, because users\' interpretations of the meaning of icons involve various cognitive processes and perceptual mechanisms. Moreover, these perceptual mechanisms are affected not only by the icons themselves, but by usage scenarios. Thus, we need a means of sensitively and continuously measuring users\' different cognitive processes when they are interacting with icons. In this study, we propose an EEG-based approach to icon evaluation, in which users\' EEG signals are measured in multiple usage scenarios. Based on a combination of EEG and behavioral results, we provide a novel interpretation of the participants\' perception during these tasks, and identify some important implications for icon design.
An EOG-Based Automatic Sleep Scoring System and Its Related Application in Sleep Environmental Control
(2014-01-01)
Kuo, Chih-En
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Liang, Sheng-Fu
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Lee, Yi-Chieh
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Cherng, Fu-Yin
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Lin, Wen-Chieh
Human beings spend approximately one third of their lives sleeping. Conventionally, to evaluate a subjects sleep quality, all-night polysomnogram (PSG) readings are taken and scored by a well-trained expert. Unlike a bulky PSG or EEG recorder on the head, the development of an electrooculogram (EOG)-based automatic sleep-staging system will enable physiological computing systems (PhyCS) to progress toward easy sleep and comfortable monitoring. In this paper, an EOG-based sleep scoring system is proposed. EOG signals are also coupling some of sleep characteristics of EEG signals. Compared to PSG or EEG recordings, EOG has the advantage of easy placement, and can be operated by the user individually at home. The proposed method was found to be more than 83% accurate when compared with the manual scorings applied to sixteen subjects. In addition to sleep-quality evaluation, the proposed system encompasses adaptive brightness control of light according to online monitoring of the users sleep stages. The experiments show that the EOG-based sleep scoring system is a practicable solution for homecare and sleep monitoring due to the advantages of comfortable recording and accurate sleep staging.
Animating rising up from various lying postures and environments
(2012-04-01)
Lin, Wen-Chieh
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Huang, Yi-Jheng
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資訊工程學系
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多媒體工程研究所
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Department of Computer Science
Generating rising up motions is an important problem but has less been addressed in computer animation. This problem is challenging as rising motions involve complex motor skills and exhibit wide varieties due to various lying postures and environments. In this paper, we present an approach that utilizes motion planning and dynamics filtering to produce physically plausible rising motions. Our motion planning algorithm connects a given posture to a closest posture in a database of 14 rising motions. Then the dynamics filtering generates a physically plausible motion from a planned motion path. Our experiments show that a variety of motions of rising from various lying postures and different environments with obstacles can be generated easily by our approach.
Attention-based high dynamic range imaging
(2011-06-01)
Lin, Wen-Chieh
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Yan, Zhi-Cheng
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資訊工程學系
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多媒體工程研究所
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Department of Computer Science
Many tone mapping algorithms have been proposed based on the studies in Human Visual System; however, they rarely addressed the effects of attention to contrast response. As attention plays an important role in human visual system, we proposed a local tone mapping method that respects both attention and adaptation effects. We adopt the High Dynamic Range (HDR) saliency map to compute an attention map, which predicts the attentive regions and nonattentative regions in an HDR image. The attention map is then used to locally adjust the contrast of the HDR image according to attention and adaptation models found in psychophysics. We applied our tone mapping approach to HDR images and videos and compared with the results generated by three state-of-the-art tone mapping algorithms. Our experiments show that our approach produces results with better image quality in terms of preserving details and chromaticity of visual saliency.
Automated recognition system to classify subcellular protein localizations in images of different cell lines acquired by different imaging systems
(2008-04-01)
Tsai, Yuh-Show
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Chung, I-Fang
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Simpson, Jeremy C.
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Lee, Mei-I
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Hsiung, Chia-Cheng
Systemic analysis of subcellular protein localization (location proteomics) provides clues for understanding gene functions and physiological condition of the cells. However, recognition of cell images of subcellular structures highly depends on experience and becomes the rate-limiting step when classifying subcellular protein localization. Several research groups have extracted specific numerical features for the recognition of subcellular protein localization, but these recognition systems are restricted to images of single particular cell line acquired by one specific imaging system and not applied to recognize a range of cell image sources. In this study, we establish a single system for automated subcellular structure recognition to identify cell images from various sources. Two different sources of cell images, 317 Vero (http://gfp-cdna.embl.de) and 875 CHO cell images of subcellular structures, were used to train and test the system. When the system was trained by a single source of images, the recognition rate is high and specific to the trained source. The system trained by the CHO cell images gave high average recognition accuracy for CHO cells of 96%, but this was reduced to 46% with Vero images. When we trained the system using a mixture of CHO and Vero cell images, an average accuracy of recognition reached 86.6% for both CHO and Vero cell images. The system can reject images with low confidence and identify the cell images correctly recognized to avoid manual reconfirmation. In summary, we have established a single system that can recognize subcellular protein localizations from two different sources for location-proteomic studies.
Block-based Feature Extraction Model for Early Fire Detection
(2017-01-01)
Chou, Kuang-Pen
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Prasad, Mukesh
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Gupta, Deepak
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Sankar, Sharmi
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Xu, Ting-Wei
Every year the fire disaster always causes a lot of casualties and property damage. Many researchers are involved in the study of related disaster prevention. Early warning systems and stable fire can significantly reduce the damage caused by fire. Many existing image-based early warning systems can perform well in a particular field. In this paper, we propose a general framework that can be applied in most realistic environments. The proposed system is based on a block-based feature extraction method, which analyses local information in separate regions leading to a reduction in computing data. Local features of fire block are extracted from the detailed characteristics of fire objects, which include fire color, fire source immobility, and disorder. Each local feature has high detection rate and filter out different false-positive cases. Global analysis with fire texture and non-moving properties are applied to further reduce false alarm rate. The proposed system is composed of algorithms with low computation. Through a series of experiments, it can be observed that Experimental results show that the proposed system has higher detection rate and low false alarm rate under various environment.
Chess Evolution Visualization
(2014-05-01)
Lu, Wei-Li
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Wang, Yu-Shuen
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Lin, Wen-Chieh
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資訊工程學系
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Department of Computer Science
We present a chess visualization to convey the changes in a game over successive generations. It contains a score chart, an evolution graph and a chess board, such that users can understand a game from global to local viewpoints. Unlike current graphical chess tools, which focus only on highlighting pieces that are under attack and require sequential investigation, our visualization shows potential outcomes after a piece is moved and indicates how much tactical advantage the player can have over the opponent. Users can first glance at the score chart to roughly obtain the growth and decline of advantages from both sides, and then examine the position relations and the piece placements, to know how the pieces are controlled and how the strategy works. To achieve this visualization, we compute the decision tree using artificial intelligence to analyze a game, in which each node represents a chess position and each edge connects two positions that are one-move different. We then merge nodes representing the same chess position, and shorten branches where nodes on them contain only two neighbors, in order to achieve readability. During the graph rendering, the nodes containing events such as draws, effective checks and checkmates, are highlighted because they show how a game is ended. As a result, our visualization helps players understand a chess game so that they can efficiently learn strategies and tactics. The presented results, evaluations, and the conducted user studies demonstrate the feasibility of our visualization design.
Comparative Evaluation of the Effects of Motion Control on Cybersickness in Immersive Virtual Environments
(2020-01-01)
Venkatakrishnan, Roshan
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Venkatakrishnan, Rohith
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Bhargava, Ayush
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Lucaites, Kathryn
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Solini, Hannah
The commercialization and lowering costs of consumer grade Virtual Reality (VR) devices has made the technology increasingly accessible to users around the world. The usage of VR technology is often accompanied by an undesirable side effect called cybersickness. Cybersickness is the feeling of discomfort that occurs during VR experiences, producing symptoms similar to those of motion sickness. It continues to remain one of the biggest hurdles to the widespread adoption of VR, making it increasingly important to explore and understand the factors that influence its onset. In this work, we investigated the influence of the presence/absence of motion control on the onset and severity of cybersickness in an HMD based VR driving simulation employing steering as a travel metaphor. Towards this end, we conducted a between subjects study manipulating the presence of control between three experimental conditions, two of which (Driving condition and Yoked Pair condition) formed a yoked control design where every pair of drivers and their yoked pairs were exposed to identical vehicular motion stimuli created by participants in the driving condition. In the other condition (Autonomous Car condition), participants experienced a program driven autonomous vehicle simulation. Results indicated that participants in the Driving condition experienced higher levels of cybersickness than participants in the Yoked Pair condition. While these results don't conform to findings from previous research which suggests that having control over motion reduces cybersickness, it seems to point towards the importance of the fidelity of the control metaphor's feedback response in alleviating cybersickness. Simply allowing one control their motion may not readily alleviate cybersickness but could instead increase it in such HMD based VR driving simulations. It may hence be important to consider how well the control metaphor and its feedback matches users' expectations if we want to successfully mitigate cybersickness.
Deep Sparse Representation Classifier for facial recognition and detection system
(2019-07-01)
Cheng, Eric-Juwei
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Chou, Kuang-Pen
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Rajora, Shantanu
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Jin, Bo-Hao
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Tanveer, M.
This paper proposes a two-layer Convolutional Neural Network (CNN) to learn the high-level features which utilizes to the face identification via sparse representation. Feature extraction plays a vital role in real-world pattern recognition and classification tasks. The details description of the given input face image, significantly improve the performance of the facial recognition system. Sparse Representation Classifier (SRC) is a popular face classifier that sparsely represents the face image by a subset of training data, which is known as insensitive to the choice of feature space. The proposed method shows the performance improvement of SRC via a precisely selected feature exactor. The experimental results show that the proposed method outperforms other methods on given datasets. (C) 2019 Elsevier B.V. All rights reserved.
Deformation Simulation Based on Model Reduction with Rigidity Sampling
(2017-01-01)
Chien, Shuo-Ting
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Hu, Chen-Hui
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Huang, Cheng-Yang
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Tsai, Yu-Ting
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Lin, Wen-Chieh
Deformation simulation based on model reduction with rigidity-guided sampling
(2018-06-01)
Chien, Shuo-Ting
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Hu, Chen-Hui
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Huang, Cheng-Yang
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Tsai, Yu-Ting
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Lin, Wen-Chieh
The deformation results of previous model reduction methods with external forces applied show noticeable differences from full-scale finite element method (FEM) simulation. We found that data-driven approaches, specifically proper orthogonal decomposition, can be a solution to this nonlinear deformation simulation problem in the subspace. Nevertheless, off-line FEM simulation with an infinite number of possible input forces at different locations makes it infeasible if no prior information is given. We propose rigidity-guided sampling to efficiently select the points of application of forces (force sample points) to construct more effective and compact subspace bases, thereby improving the simulation accuracy of reduced deformable models with applied external forces and still retaining fast run-time performance. The key idea of our approach is that distinct deformations of an object at different force sample points can be estimated prior to FEM simulation. By selecting the force sample points with distinct deformations, the computational cost of off-line FEM simulation can be reduced significantly. Our run-time deformation results are much closer to the full-scale FEM simulation with external forces applied, compared to the results of using only the modal derivative bases while the speedup over full-scale simulation is still substantial.